MaMiC: Macro and Micro Curriculum for Robotic Reinforcement Learning
This addresses the problem of exploratory constraints in robotic reinforcement learning for manipulation tasks, offering an incremental improvement over existing curriculum methods.
The paper tackles robotic manipulation tasks with sparse rewards by proposing MaMiC, a dual curriculum scheme combining macro and micro curricula to guide learning without engineered rewards, and demonstrates its performance on Fetch environments.
Shaping in humans and animals has been shown to be a powerful tool for learning complex tasks as compared to learning in a randomized fashion. This makes the problem less complex and enables one to solve the easier sub task at hand first. Generating a curriculum for such guided learning involves subjecting the agent to easier goals first, and then gradually increasing their difficulty. This paper takes a similar direction and proposes a dual curriculum scheme for solving robotic manipulation tasks with sparse rewards, called MaMiC. It includes a macro curriculum scheme which divides the task into multiple sub-tasks followed by a micro curriculum scheme which enables the agent to learn between such discovered sub-tasks. We show how combining macro and micro curriculum strategies help in overcoming major exploratory constraints considered in robot manipulation tasks without having to engineer any complex rewards. We also illustrate the meaning of the individual curricula and how they can be used independently based on the task. The performance of such a dual curriculum scheme is analyzed on the Fetch environments.